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Atoms is building Physical AI—real-world robots for industries that move civilization forward, starting with food, mining, and transport. The company integrates hardware, software, AI, operations, and manufacturing to deploy autonomous systems into real environments at scale.
As a Robotics Controls Engineer, you will develop and maintain core control systems for autonomous haul trucks operating in mining environments. You'll own the full stack of vehicle control: localization and state estimation, longitudinal control, lateral control, and navigation systems for 200+ ton vehicles.
Key responsibilities:
- Design, implement, and tune control algorithms for autonomous vehicle systems
- Build localization and state estimation pipelines that fuse multiple sensor modalities (GPS/RTK, IMUs, CAN interfaces)
- Develop longitudinal and lateral control systems using classical control theory (PID, lead/lag compensation) and advanced techniques (Model Predictive Control)
- Implement path-following algorithms (Stanley, Pure Pursuit) and navigation stacks
- Analyze system performance through simulation and field testing on actual haul trucks
- Debug control issues using logged data and identify root causes
- Collaborate with perception, planning, and hardware teams to integrate control systems
- Write production-quality Python code (with C++ for optimization-critical paths) that runs reliably on NVIDIA Jetson hardware
- Periodic travel to customer mining sites (up to 15%) and schedule flexibility during deployments
Required qualifications:
- BS/MS/PhD in Robotics, Mechanical Engineering, Aerospace Engineering, Electrical Engineering, or related field
- 2+ years of professional software development experience
- Strong foundation in classical control theory and stability analysis
- Experience with state estimation (Kalman filters, EKF/UKF)
- Proficiency in Python and NumPy/SciPy
- Understanding of vehicle dynamics and kinematics
- Experience deploying localization and controls algorithms on real-world systems
Desired experience includes Model Predictive Control, signal processing, system identification, heavy equipment/off-highway vehicles, CAN bus interfaces, ROS, and modern ML techniques for controls.
